Clinical Research Institute, First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing, China.
Int J Epidemiol. 2023 Jun 6;52(3):942-951. doi: 10.1093/ije/dyac239.
Prevalence estimates are fundamental to epidemiological studies. Although they are highly vulnerable to misclassification bias, the risk of bias assessment of prevalence estimates is often neglected. Quantitative bias analysis (QBA) can effectively estimate misclassification bias in epidemiological studies; however, relatively few applications are identified. One reason for its low usage is the lack of knowledge and tools for these methods among researchers. To expand existing evaluation methods, based on the QBA principles, three indicators are proposed. One is the relative bias that quantifies the bias direction through its signs and the bias magnitude through its quantity. The second is the critical point of positive test proportion in case of a misclassification bias that is equal to zero. The third is the bound of positive test proportion equal to adjusted prevalence at misclassification bias level α. These indicators express the magnitude, direction and uncertainty of the misclassification bias of prevalence estimates, respectively. Using these indicators, it was found that slight oscillations of the positive test proportion within a certain range can lead to substantial increases in the misclassification bias. Hence, researchers should account for misclassification error analytically when interpreting the significance of adjusted prevalence for epidemiological decision making. This highlights the importance of applying QBA to these analyses. In this article, we have used three real-world cases to illustrate the characteristics and calculation methods of presented indicators. To facilitate application, an Excel-based calculation tool is provided.
患病率估计是流行病学研究的基础。尽管它们极易受到分类错误偏倚的影响，但患病率估计的偏倚风险评估往往被忽视。定量偏倚分析（QBA）可以有效地估计流行病学研究中的分类错误偏倚；然而，很少有应用被识别。其使用率低的原因之一是研究人员对这些方法缺乏了解和工具。为了扩展现有的评估方法，基于 QBA 原则，提出了三个指标。一个是相对偏差，通过其符号量化偏差方向，通过其数量量化偏差幅度。第二个是分类错误偏倚等于零时阳性测试比例的临界点。第三个是在分类错误偏倚水平 α 处等于调整后患病率的阳性测试比例的上限。这些指标分别表示患病率估计的分类错误偏倚的大小、方向和不确定性。使用这些指标发现，阳性测试比例在一定范围内的轻微波动可能导致分类错误偏倚的大幅增加。因此，研究人员在解释调整后患病率对流行病学决策的意义时，应在分析中考虑分类错误。这突出了将 QBA 应用于这些分析的重要性。在本文中，我们使用了三个真实案例来说明所提出指标的特征和计算方法。为了便于应用，提供了一个基于 Excel 的计算工具。
